class_docstr: Class generalizing forward beam search for seq2seq models.
  
  Also provides types to specify the recurrent structure of decoding:
  
  StateConfig:
      initial_value: blob providing value of state at first step_model
      state_prev_link: LinkConfig describing how recurrent step receives
          input from global state blob in each step
      state_link: LinkConfig describing how step writes (produces new state)
          to global state blob in each step
  
  LinkConfig:
      blob: blob connecting global state blob to step application
      offset: offset from beginning of global blob for link in time dimension
      window: width of global blob to read/write in time dimension
code: def get_previous_tokens(self):
      return self.tokens_t_prev_int32_flattened
  
file_path: pytorch/caffe2/python/models/seq2seq/beam_search.py
imports: from collections import namedtuple
  from caffe2.python import core
  import caffe2.python.models.seq2seq.seq2seq_util
  from caffe2.python.models.seq2seq.seq2seq_model_helper import Seq2SeqModelHelper
label_desc: Use image node_id 3 for calling the BeamSearchForwardOnly obj's underlying member method code with example usage: obj.get_previous_tokens() and returns: self